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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
Data Processing and Transformations	- Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering) - User-defined functions (UDFs) - PySpark DataFrame transformations
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data - ETL patterns and transformations
Data Governance and Quality	- Data quality concepts and management - Unity Catalog basics - Data access control and governance
Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q318-Q323):

質問 # 318
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several JSON files from containers on a storage account as soon as the file arrives within the storage account. Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.readStream.format("json".load("input/path")
B. df = spark.readStream.format("cloud").option("json").load("/input/path")
C. df = spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("/input/path")
D. df = spark.read.json("input/path")

正解：C

解説：
To automatically ingest JSON files as soon as they arrive in cloud storage, the correct syntax is to use Auto Loader with spark.readStream.format("cloudFiles").option("cloudFiles.format",
"json").load("/input/path"). This enables continuous ingestion of new files into a streaming dataframe.

質問 # 319
A Databricks Job with four sequential tasks is executed. The job fails at Task 3. After the root cause is fixed, the data engineer needs to complete the workflow without rerunning the successful Tasks 1 and 2.
Which action on the Job Run details page allows the engineer to resume execution from the point of failure?

A. Reset job
B. Clone task
C. Run now
D. Repair run

正解：D

質問 # 320
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

正解：B

解説：
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution

質問 # 321
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

A. Option E
B. Option C
C. Option A
D. Option D
E. Option B

正解：A

解説：
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

質問 # 322
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

正解：D

解説：
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

質問 # 323
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